Unimodular Polynomial Matrices over Finite Fields
Akansha Arora, Samrith Ram, Ayineedi Venkateswarlu

TL;DR
This paper investigates the properties and enumeration of unimodular polynomial matrices over finite fields, providing new proofs and resolving conjectures related to their probability and structure.
Contribution
It offers a novel proof of existing results using control theory and resolves open questions about the probability of unimodularity in matrix polynomials.
Findings
Confirmed the probability that a matrix polynomial is unimodular
Provided a new proof of a theorem on splitting subspaces
Resolved a conjecture on unimodular polynomial matrices
Abstract
We consider some combinatorial problems on matrix polynomials over finite fields. Using results from control theory we give a proof of a result of Helmke, Jordan and Lieb on the number of linear unimodular matrix polynomials over a finite field. As an application of our results we give a new proof of a theorem of Chen and Tseng which answers a question of Niederreiter on splitting subspaces. We use our results to affirmatively resolve a conjecture on the probability that a matrix polynomial is unimodular.
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Taxonomy
TopicsCoding theory and cryptography · graph theory and CDMA systems · Advanced Topics in Algebra
Unimodular Polynomial Matrices over Finite Fields
Akansha Arora
Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), New Delhi 110020, India.
,
Samrith Ram
Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), New Delhi 110020, India.
and
Ayineedi Venkateswarlu
Computer Science Unit, Indian Statistical Institute - Chennai Centre, Chennai 600029, India.
Abstract.
We consider some combinatorial problems on matrix polynomials over finite fields. Using results from control theory we give a proof of a result of Lieb, Jordan and Helmke on the number of linear unimodular matrix polynomials over a finite field. As an application of our results we give a new proof of a theorem of Chen and Tseng which answers a question of Niederreiter on splitting subspaces. We use our results to affirmatively resolve a conjecture on the probability that a matrix polynomial is unimodular.
Key words and phrases:
unimodular matrix polynomial, unimodular matrix, splitting subspace, controllable pair, irreducible polynomial, finite field
2010 Mathematics Subject Classification:
93B05, 93B07, 15B33, 15A22, 15A83
Contents
- 1 Introduction
- 2 Simple Linear Transformations
- 3 Splitting Subspaces
- 4 Probability of Unimodular Polynomial Matrices
1. Introduction
Denote by the finite field with elements where is a prime power. Let denote the ring of polynomials over in the indeterminate . For any ring and positive integers define to be the set of all matrices over . Similarly denotes the ring of matrices over . Denote by the matrix in whose th entry is zero whenever and equal to 1 for .
The main objects of study in this paper are matrix polynomials over finite fields. A matrix polynomial over a field in the variable is a sum , where for some fixed positive integers . It is often convenient to view such a matrix polynomial as a single matrix whose entries are polynomials in (sometimes referred to as a polynomial matrix) and we freely alternate between these two points of view. A matrix polynomial is unimodular if the greatest common divisor of all minors of is equal to 1 where . The notion of unimodularity can be defined more generally for rectangular matrices over an arbitrary integral domain. A landmark result in the setting of unimodularity is the Quillen-Suslin theorem [22, 25] formerly known as Serre’s conjecture. We refer to [13, 18, 20, 26] for other contexts where unimodularity is considered. We begin with a combinatorial question concerning matrix polynomials over a finite field.
Question 1.1**.**
Given positive integers and a prime power , determine the number of matrices for which the matrix polynomial is unimodular.
This question was essentially considered by Kocięcki and Przyłuski [16] (also see [24, Prob. 1.2]) in an attempt to determine the number of reachable pairs of matrices over a finite field. Reachability is a fundamental notion in the control theory of linear systems. The question was fully answered only recently by Lieb, Jordan and Helmke [19, Thm. 1] who showed that the answer is equal to . We refer to the introduction of [24] for details and alternate formulations of the result of Lieb et al. Our main result is Lemma 2.10 which allows us to give a new proof (Corollary 2.13) of the theorem of Lieb et al. An essential ingredient in our main lemma is a control theoretic result of Brunovský on completely controllable pairs.
Further applications of our results appear in Sections 3 and 4. In Section 3 we consider splitting subspaces (defined below) which were introduced by Niederreiter [21, Def. 1] in the context of his work on the multiple recursive matrix method for pseudorandom number generation.
Definition 1.2**.**
Let be positive integers and consider the vector space over . For any element an -dimensional subspace of is -splitting if
[TABLE]
Niederreiter was interested in the following question on splitting subspaces.
Question 1.3**.**
Given such that , what is the number of -splitting subspaces of of dimension ?
It may be noted that the same question was also considered by Goresky and Klapper (see the remark in [12, p. 1653] and [12, Thm. 3(4)]). In addition to the evident cryptographic aspect, Niederreiter’s question also has interesting connections with group theory and finite projective geometry via block companion Singer cycles. We refer to [8, 9] for more on this topic. The case of Niederreiter’s question was settled in [9] using a result that answers the following question: What is the probability that two randomly chosen polynomials of a fixed positive degree over a finite field are coprime? This question on the probability of coprime polynomials goes back to an exercise in Knuth [15, §4.6.1, Ex. 5] and has subsequently been considered by Corteel, Savage, Wilf and Zeilberger [4] in the more general setting of combinatorial prefabs. Further results on the degree distribution of the greatest common divisor of random polynomials over a finite field appear in [6]. In fact, our main result relies on Lemma 2.4 which may be viewed as a probabilistic result on coprime polynomials. Chen and Tseng [2, Cor. 3.4] eventually answered Niederreiter’s question on splitting subspaces by proving the following theorem which was initially conjectured in [9, Conj. 5.5].
Theorem 1.4** (Splitting Subspace Theorem).**
For any such that , the number of -splitting subspaces of of dimension is precisely
[TABLE]
In this paper a control-theoretic result of Wimmer (Theorem 3.8) is used to prove Theorem 3.9 from which the Splitting Subspace Theorem follows as a corollary. In Section 4 a generalization of Question 1.1 is considered. The answer to this question which was stated earlier can be given a probabilistic flavour as follows.
Theorem 1.5**.**
If a matrix is selected uniformly at random from , then the probability that is unimodular is given by .
Using results in Section 2, we prove a conjecture (Theorem 4.1) proposed in [24] on the proportion of unimodular polynomial matrices which generalizes Theorem 1.5.
2. Simple Linear Transformations
We begin by recalling the notion of a simple linear transformation [24, Def. 3.1].
Definition 2.1**.**
Let denote a vector space over a field and let be a subspace of . An -linear transformation is simple if the only -invariant subspace properly contained in is the zero subspace.
Remark 2.2**.**
Note that the definition requires that there are no -invariant subspaces properly contained in rather than in . The reason being that if is a proper subspace, then the definition does not allow itself to be -invariant. In the case we necessarily have that is -invariant. It can be shown that a linear operator on a finite dimensional vector space is simple if and only if it has an irreducible characteristic polynomial. In fact simple maps defined on a proper subspace of a vector space are precisely the restrictions to of simple maps defined on all of .
The following proposition elucidates the connection between simple linear transformations and unimodularity.
Proposition 2.3**.**
Let be an -dimensional vector space over with ordered basis . Let denote the ordered basis for the subspace spanned by . Let be a linear transformation and let denote the matrix of with respect to and . Then is simple if and only if is unimodular.
Proof.
See [24, Prop. 2.5] and [24, Prop. 3.2]. ∎
Let be a positive integer and let be an arbitrary but fixed nonzero vector. Let be the largest index such that . Let be a nonincreasing sequence of integers with . Let denote the number of -tuples of polynomials over such that and for with . Here we interpret negative powers of to be zero. Since , we necessarily have for any tuple . We adopt the convention that the degree of the zero polynomial is . Note that if there is some such that for each , then where .
We adapt an argument in the proof of [7, Thm. 4.1] to prove the following lemma which is central to our main result.
Lemma 2.4**.**
Let be a positive integer and let be a sequence of integers. Let be a fixed nonzero vector. We have
[TABLE]
where .
Proof.
Fix a positive integer . Let denote the set of ordered -tuples where for some with for . Let be the largest index such that . We partition into disjoint subsets where the set denotes the set of -tuples in whose GCD is a monic polynomial of degree . For each monic polynomial over of degree and any coprime -tuple of polynomials in , it is easy to see that . Conversely, for any tuple , the polynomial is monic of degree and is an ordered -tuple of coprime polynomials in . As a result, we have for . For , we have
[TABLE]
Replacing by for each , we obtain
[TABLE]
where the last equality follows from (1). It follows that , or equivalently, as desired. ∎
As the language of control theory is used in the proof of our main result we collate here a few definitions [11, IX.2] and results that are referred to later on. In what follows, denotes an arbitrary field and are fixed positive integers.
Definition 2.5**.**
A matrix pair is a reachable pair if the matrix has rank equal to .
Remark 2.6**.**
A pair is reachable if and only if the polynomial matrix is unimodular.
Definition 2.7**.**
Associate with each pair a sequence of integers by defining and for ,
[TABLE]
Consider the dual sequence defined by The numbers are called the controllability indices of the pair .
For any positive integer , denote by the general linear group of nonsingular matrices over . Define [28, P. 3]
[TABLE]
Definition 2.8**.**
Two pairs and in are said to be -equivalent [28, Def. 2.1] if there exists a matrix such that for each pair of matrices and , there exist matrices and such that
[TABLE]
When the values of are clear from the context, we refer to -equivalence simply as -equivalence. The following result ([1], [28, Lem. 2.7]) is due to Brunovsky.
Theorem 2.9**.**
Let . Suppose is a reachable pair with and are the controllability indices of . Then is -equivalent to a pair of the following form:
- i)
is the block diagonal matrix where is the matrix
[TABLE] 2. ii)
is of the block form , where denotes the matrix
[TABLE]
and denotes the th row of the identity matrix.
The following lemma is our main result.
Lemma 2.10**.**
Let be integers with . Let be an -dimensional vector space over and let be fixed subspaces of of dimensions and respectively with . Suppose is a simple linear transformation. Then the number of simple linear transformations such that (the restriction of to is ) is equal to .
Proof.
First suppose . In this case is spanned by some nonzero vector . Then is simple precisely when does not lie in the span of . So the number of such linear transformations is clearly .
Suppose . Let be an ordered basis for and be an ordered basis for obtained by extending . Let be the matrix of with respect to and . Since is simple, is unimodular by Proposition 2.3. Suppose that Y=\left[\!\!\begin{array}[]{c}A\\ C\\ \end{array}\!\!\right] for some and . Since is unimodular, it follows by Remark 2.6 that is a reachable pair. Suppose that , and are the controllability indices of the pair . We have . By Theorem 2.9 we may assume that and are of the following form:
, where is the matrix \left[\begin{array}[]{cc}{\bf 0}&0\\ I_{k_{i}-1}&{\bf 0}\\ \end{array}\right];
C=\left[\!\!\begin{array}[]{c}C^{\prime}\\ {\bf 0}\\ \end{array}\!\!\right], where with and denotes the th column of the identity matrix for . Let for and set . Then the linear transformation can be described by
[TABLE]
where . Also the matrix can be described by
[TABLE]
where is the th column of the identity matrix . Let be the subspace of spanned by . We have .
Now and is of dimension . Since , there is a nonzero vector . Let be an ordered basis for . Since , we have is an ordered basis for . Let be the matrix of the identity map on with respect to the bases and . Note that the matrix can be expressed as
[TABLE]
where is the matrix of the identity map on with respect to the bases and . Let for some scalars . Then the first column of is given by . The matrix of with respect to and is given by . Define and let be the matrix of with respect to the bases and . Since we have for some column vector . By Proposition 2.3, is simple if and only if is unimodular. Let , where is the submatrix formed by the first columns of . We have , where .
Suppose . Then the matrix is of the form
[TABLE]
where for , , and .
Now consider the polynomial matrix . We permute the rows of in the following way: for each , arrange the th row of in between the th and th block rows appearing in (9). The resulting matrix is of the following form:
[TABLE]
where {\bf Z}_{i}=x\left[\!\!\begin{array}[]{c}I_{k_{i}}\\ {\bf 0}\\ \end{array}\!\!\right]-\left[\!\!\begin{array}[]{c}{\bf 0}\\ I_{k_{i}}\\ \end{array}\!\!\right], {\bf b}_{i}^{\prime}=\left[\!\!\begin{array}[]{c}-{\bf b}_{i}\\ c_{k+i}x-b_{k+i}\\ \end{array}\!\!\right] for . Now we apply the following sequence of elementary row operations to to eliminate in the first columns: in the first block row appearing in (15), add times the th row to the th row successively for in that order. Similarly we apply elementary row operations to the other block rows. By appropriate elementary column operations, the entries in the last column can be made zero at suitable positions. Eventually we can transform the matrix to the following form:
[TABLE]
where {\bf Z}_{i}^{\prime}=-\left[\!\!\begin{array}[]{c}{\bf 0}\\ I_{k_{i}}\\ \end{array}\!\!\right], {\bf b}_{i}^{\prime\prime}=\left[\!\!\begin{array}[]{c}f_{i}\\ {\bf 0}\\ \end{array}\!\!\right] with for and .
Let . The matrix is unimodular if and only if . By Lemma 2.4 it follows that the number of vectors such that is given by . As and are equivalent, the result follows.
∎
The lemma can be recast in the setting of matrices as follows.
Corollary 2.11**.**
Let be such that the linear matrix polynomial is unimodular. For each column vector let . Then the number of column vectors for which is unimodular equals .
We can now give an alternate proof of [24, Thm. 3.8] concerning the number of simple linear transformations with a fixed domain.
Corollary 2.12**.**
Let be an -dimensional vector space over and be a proper -dimensional subspace of . The number of simple linear transformations equals
[TABLE]
We may use Proposition 2.3 to reformulate the corollary in terms of matrices. This allows us to answer Question 1.1 stated in the introduction.
Corollary 2.13**.**
Let be positive integers with . The number of matrices such that is unimodular equals
[TABLE]
By repeated application of Corollary 2.11 we obtain the following extension which is used later on in Sections 3 and 4.
Lemma 2.14**.**
Let be positive integers such that . Suppose that the matrix polynomial is unimodular for some . The number of matrices such that the matrix polynomial
[TABLE]
is unimodular is equal to .
3. Splitting Subspaces
Recall the definition of splitting subspace given earlier in the introduction.
Definition 3.1**.**
Let be positive integers and consider the vector space over . For any element an -dimensional subspace of is -splitting if
[TABLE]
Closely related to splitting subspaces are block companion matrices which we define below.
Definition 3.2**.**
For positive integers , an -block companion matrix over is a matrix in of the form
[TABLE]
where and denotes the identity matrix over while denotes the zero matrix in .
Remark 3.3**.**
It was shown (see the discussion after Conjecture 5.5 in [9] or Appendix A in [10] for an overview) that the Splitting Subspace Theorem is in fact equivalent to the following theorem on block companion matrices.
Theorem 3.4**.**
For any irreducible polynomial of degree , the number of -block companion matrices over having as their characteristic polynomial equals
[TABLE]
It is noteworthy that the problem of counting specific types of block companion matrices having irreducible characteristic polynomial has been considered in other contexts [3, 14, 23] where pseudorandom number generation is of interest. We now deduce Theorem 3.4 as a special case of Theorem 3.9 which we prove below, thereby providing an alternate proof of the Splitting Subspace Theorem.
Definition 3.5**.**
For positive integers with , let denote the matrix given by
[TABLE]
Lemma 3.6**.**
The linear matrix polynomial
[TABLE]
is unimodular.
Proof.
Since the minor formed by the last rows of the above matrix polynomial equals it follows that the GCD of all minors is 1. ∎
Definition 3.7**.**
Let be positive integers such that . An -companion matrix of order over is a square matrix of the form
[TABLE]
for some . We denote the set of all -companion matrices of order over by . Note that .
Let denote the set of all monic polynomials of degree over . Now consider the map given by
[TABLE]
To determine the size of the fibers of , we require a theorem of Wimmer.
Theorem 3.8** (Wimmer).**
Let be an arbitrary field and let . Suppose is a monic polynomial of degree and let be the invariant factors of the polynomial matrix . There exists a matrix such that the block matrix has characteristic polynomial if and only if the product divides .
Proof.
See Wimmer [27] or Cravo [5, Thm. 15]. ∎
Theorem 3.9**.**
Suppose that is irreducible. Then
[TABLE]
Proof.
Let with , where the ’s are the columns of . Let and let denote the submatrix of formed by the first columns for . Suppose that . Since is irreducible, it follows by Lemma 3.6 and Wimmer’s theorem that the linear matrix polynomials
[TABLE]
are unimodular for . Conversely, if are chosen such that the matrix polynomials in (23) are unimodular, then there is a unique choice of for which . This follows since there are total choices for and for each monic polynomial of degree , Wimmer’s theorem ensures that there exists some choice of such that the characteristic polynomial is . By Lemma 2.14 it follows that the number of choices for the first columns of is equal to which proves the result. ∎
Remark 3.10**.**
In the case where divides , say , the set consists precisely of all -block companion matrices over . This observation yields the following corollary stated earlier as Theorem 3.4.
Corollary 3.11**.**
For any irreducible polynomial of degree , the number of -block companion matrices over having as their characteristic polynomial equals
[TABLE]
Proof.
It follows by the above remark that the number of -block companion matrices over having as their characteristic polynomial equals
[TABLE]
which is clearly equal to the given product. ∎
In light of the above corollary and Remark 3.3 we can view Theorem 3.9 as a more general result than the Splitting Subspace Theorem. While our proof relies on results in control theory, it is shorter than the proofs of the theorem appearing in [2] and [17].
4. Probability of Unimodular Polynomial Matrices
We apply Lemma 2.14 to positively resolve a conjecture [24, Conj. 4.1] concerning the number of unimodular polynomial matrices. For positive integers with , define
[TABLE]
Theorem 4.1**.**
The probability that a uniformly random element of is unimodular is given by .
Proof.
To each element in , we associate the corresponding -tuple of its coefficients . Now consider the matrix
[TABLE]
of dimension . Let
[TABLE]
By adding times the th block row to the th block row successively for in and using suitable column block operations, we obtain
[TABLE]
where . Observe that is equivalent to . So the invariant factors of and are the same. Therefore is unimodular if and only if is unimodular. By Lemma 2.14, the number of ways to choose the last columns of the matrix in (28) in such a way that is unimodular is
[TABLE]
On the other hand, the cardinality of is clearly and therefore the probability that a uniformly random element of is unimodular is precisely . ∎
Note that the probability computed in the theorem is independent of .
Remark 4.2**.**
The above theorem is a generalization of Corollary 2.13 which is evidently the special case .
Theorem 4.1 parallels a result of Guo and Yang [13, Thm. 1] who prove that the natural density of unimodular matrices over is precisely .
Remark 4.3**.**
To study the invariant factors of an element , it suffices to study those of the corresponding linear matrix polynomial associated to the matrix as defined in Equation (28). The matrix polynomial is called the linearization of .
Acknowledgements
The third author would like thank Mr. Abhishek Kesarwani and Dr. Santanu Sarkar for useful discussions.
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